Compute WUIs (no IPA) - KD6 Essential
Mervin M Fansler
06 October 2022
Purpose
Using the bulk summarized GWPS data object, compute the WUI for each mulit-UTR gene in each perturbation. Additionally, compute average WUI for each perturbation and analyze for outliers.
Note that WUI can only be computed for detected genes. Targets with low cell recovery tend to recover fewer genes and will rely on higher expression genes. If these have a different WUI profile than lower expression genes, there could be a bias in the average WUI computed for the target. Currently, we do not attempt to model this.
Initialization
Libraries
library(magrittr)
library(tidyverse)
library(cowplot)
library(plotly)
library(ggbeeswarm)
library(SummarizedExperiment)
library(Matrix)
library(sparseMatrixStats)
library(clusterProfiler)
library(org.Hs.eg.db)Parameters
set.seed(20210818)
MIN_UMIS=1000
MIN_MEAN_UI=0.10
FILE_SE="data/se/kd6_essential_bulk_expressed.Rds"
FILE_OUT="tbl/kd6_essential_wui_filtered-noipa-ui10.xlsx"
DIR_IMG_OUT="img/kd6-essential-filtered-noipa-ui10-"
## KNOWN FACTORS
GENES_CPSF <- c("CPSF1", "CPSF2", "CPSF3", "CPSF4", "WDR33", "FIP1L1")
GENES_CF1 <- c("NUDT21", "CPSF6", "CPSF7")
GENES_CF2 <- c("PCF11", "CLP1")
GENES_CSTF <- c("CSTF1", "CSTF2", "CSTF2T", "CSTF3")
GENES_PAF <- c("PAF1", "CTR9", "RTF1")
GENES_NEF <- c("NXF1", "SRSF3", "SRSF7", "THOC5")
COLOR_MAP <- c("non-targeting"="grey",
"CPSF complex"="#8FD979",
"CFI complex"="purple",
"CFII complex"="#FFAA66",
"CSTF complex"="#F6CDA3",
"PAF complex"="steelblue",
"mRNA export factors"="magenta",
"other"="black")Helper Functions
compute_effective_count <- function (cts) {
pct <- (cts %*% Diagonal(ncol(cts), 1/colSums(cts)))
lpct <- pct
lpct@x <- log2(lpct@x)
2^(-colSums(pct*lpct))
}Data
Loading
se_tx_target <- readRDS(FILE_SE)Preprocessing
idx_ntp <- colnames(se_tx_target) %>%
str_detect("non-targeting")
idx_multi_raw <- which(rowData(se_tx_target)$utr_type_no_ipa == 'multi' & !rowData(se_tx_target)$is_ipa)
cts_tx_ntp <- assay(se_tx_target[idx_multi_raw, idx_ntp], 'counts')
M_gene_tx <- rowData(se_tx_target[idx_multi_raw,]) %>%
as_tibble %>%
dplyr::select(transcript_id, gene_id) %>%
deframe %>%
fac2sparse
cts_gene_ntp <- M_gene_tx %*% cts_tx_ntp
ui_tx_ntp <- as.matrix(cts_tx_ntp / (t(M_gene_tx) %*% cts_gene_ntp))
ncells_ntp <- se_tx_target$n_cells[idx_ntp]
mui_tx <- rowWeightedMeans(ui_tx_ntp, ncells_ntp, na.rm=TRUE)
mui_tx[is.na(mui_tx)] <- 0
idx_tx <- M_gene_tx[,mui_tx >= MIN_MEAN_UI] %>% {
idx_i <- rowSums(.) > 1;
idx_j <- colSums(.[idx_i,]) > 0;
.[idx_i, idx_j]
} %>% colnames
df_multi <- rowData(se_tx_target[idx_tx,]) %>% as_tibble %>%
mutate(ensembl_id=str_remove(gene_id, "\\.[0-9]+$")) %>%
group_by(ensembl_id) %>%
mutate(utr_rank_expr=rank(utr_rank),
utr_wt=(utr_rank_expr - 1)/(max(utr_rank_expr) - 1)) %>%
ungroup() %>%
dplyr::select(transcript_id, transcript_name, gene_id, gene_name, ensembl_id,
utr_rank, utr_rank_expr, utr_wt) %>%
arrange(gene_id, utr_rank)
## use ordered version
idx_tx <- df_multi$transcript_id
df_multi %>% distinct(ensembl_id) %>% nrow %>%
sprintf(fmt="Found %d multi-UTR genes.")## [1] "Found 4780 multi-UTR genes."df_multi %>% nrow %>%
sprintf(fmt="These consist of %d 3' UTR isoforms.")## [1] "These consist of 11129 3' UTR isoforms."Compute WUIs
wt_tx <- df_multi %>% dplyr::select(transcript_id, utr_wt) %>% deframe
M_gene_tx <- df_multi$ensembl_id %>% fac2sparse
cts_tx_target <- assay(se_tx_target[idx_tx,], "counts")
cts_gene_target <- M_gene_tx %*% cts_tx_target
ui_tx_target <- cts_tx_target / (t(M_gene_tx) %*% cts_gene_target)
#ui_tx_target[is.na(ui_tx_target)] <- 0
wui_gene_target <- M_gene_tx %*% Diagonal(length(wt_tx), wt_tx) %*% ui_tx_targetCompute Effective Transcripts
etx_target_multi <- cts_tx_target %>% compute_effective_countTarget Analysis
df_targets <- colData(se_tx_target) %>% as_tibble %>%
mutate(mean_wui=colMeans(wui_gene_target[,sgID_AB], na.rm=TRUE),
etx_multi=etx_target_multi[sgID_AB],
n_genes_detected=colSums(!is.na(wui_gene_target[,sgID_AB]))) %>%
mutate(gene_set=case_when(
target_gene == "non-targeting" ~ "non-targeting",
target_gene %in% GENES_CPSF ~ "CPSF complex",
target_gene %in% GENES_CF1 ~ "CFI complex",
target_gene %in% GENES_CF2 ~ "CFII complex",
target_gene %in% GENES_CSTF ~ "CSTF complex",
target_gene %in% GENES_PAF ~ "PAF complex",
target_gene %in% GENES_NEF ~ "mRNA export factors",
TRUE ~ "other"
) %>% factor(levels=c("other", "non-targeting", "CPSF complex",
"CFI complex", "CFII complex", "CSTF complex",
"PAF complex", "mRNA export factors"))) %>%
mutate(is_known=!gene_set %in% c("non-targeting", "other"))Export
writexl::write_xlsx(df_targets, FILE_OUT)Plots
WUI - All Targets
g <- df_targets %>%
ggplot(aes(x="Perturbations", y=mean_wui)) +
geom_violin(fill='lightgrey', size=0.2, draw_quantiles=c(0.25, 0.50, 0.75)) +
geom_quasirandom(aes(color=gene_set, size=n_cells, text=target_gene), pch=16) +
scale_color_manual(values=COLOR_MAP) +
scale_size_continuous(range=c(0.05, 3)) +
scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
theme_bw() +
labs(x=NULL, y="Mean WUI", color="Gene Set", size=NULL)## Warning: Ignoring unknown aesthetics: textggplotly(g, tooltip=c("text", "y", "gene_set", "n_cells"))WUI vs Effective Transcripts (Multi-UTR Genes)
g <- df_targets %>%
ggplot(aes(x=mean_wui, y=etx_multi, color=gene_set, text=target_gene)) +
geom_point(aes(size=n_cells), pch=16) +
geom_rug(size=0.1) +
scale_color_manual(values=COLOR_MAP) +
scale_size_continuous(range=c(0.05, 3)) +
theme_bw() +
theme(legend.position="none") +
labs(x="Mean Weighted Usage Index",
y="Effective Transcripts (Multi-UTR Genes)",
color="Gene Set")
ggplotly(g, tooltip=c("text", "gene_set", "x", "y", "n_cells"))df_targets %>%
ggplot(aes(x=mean_wui, y=etx_multi, color=gene_set, text=target_gene)) +
geom_point(data=filter(df_targets, gene_set == 'other'), size=0.5) +
geom_point(data=filter(df_targets, gene_set == 'non-targeting'), size=0.5) +
geom_point(data=filter(df_targets, is_known), size=3) +
geom_rug(size=0.1) +
scale_color_manual(values=COLOR_MAP) +
scale_size_continuous(range=c(0.05, 3)) +
theme_bw() +
labs(x="Mean Weighted Usage Index",
y="Effective Transcripts (Multi-UTR Genes)",
color="Gene Set")
ggsave(str_c(DIR_IMG_OUT, "wui_etx_multi.pdf"), width=8, height=6, dpi=300)Gene Set Enrichment
awui_baseline <- df_targets %>%
filter(target_gene == 'non-targeting') %$%
weighted.mean(mean_wui, n_cells)
awui_targets <- df_targets %>%
filter(target_gene != 'non-targeting', n_cells >= 20) %>%
group_by(target_gene_id) %>%
summarize(mean_wui=weighted.mean(mean_wui, n_cells)) %>%
mutate(mwui_dev=mean_wui-awui_baseline) %>%
dplyr::select(target_gene_id, mwui_dev) %>% deframe %>% sort(decreasing=TRUE)
gseBP <- gseGO(awui_targets, ont="BP", OrgDb=org.Hs.eg.db, keyType="ENSEMBL", nPermSimple=1e4)## preparing geneSet collections...## GSEA analysis...## Warning in fgseaMultilevel(...): For some pathways, in reality P-values are less
## than 1e-10. You can set the `eps` argument to zero for better estimation.## leading edge analysis...## done...gseMF <- gseGO(awui_targets, ont="MF", OrgDb=org.Hs.eg.db, keyType="ENSEMBL", nPermSimple=1e4)## preparing geneSet collections...## GSEA analysis...## leading edge analysis...## done...gseCC <- gseGO(awui_targets, ont="CC", OrgDb=org.Hs.eg.db, keyType="ENSEMBL", nPermSimple=1e4)## preparing geneSet collections...## GSEA analysis...## leading edge analysis...## done...Biological Process
dotplot(gseBP, showCategory=200, split=".sign") +
facet_grid(.~.sign)
ridgeplot(gseBP, showCategory=200) +
labs(x = "Deviation from Baseline AWUI")## Picking joint bandwidth of 0.00247
emapplot(enrichplot::pairwise_termsim(gseBP), showCategory=Inf)
df_genesets <- gseBP@result
for (i in seq(nrow(df_genesets))) {
gseaplot(gseBP, by='all', geneSetID=i, title=df_genesets[i, "Description"]) %>%
print()
}
Cellular Component
dotplot(gseCC, showCategory=200, split=".sign") +
facet_grid(.~.sign)
ridgeplot(gseCC, showCategory=200) +
labs(x = "Deviation from Baseline AWUI")## Picking joint bandwidth of 0.00167
emapplot(enrichplot::pairwise_termsim(gseCC), showCategory=Inf)
df_genesets <- gseCC@result
for (i in seq(nrow(df_genesets))) {
gseaplot(gseCC, by='all', geneSetID=i, title=df_genesets[i, "Description"]) %>%
print()
}
Molecular Function
dotplot(gseMF, showCategory=200, split=".sign") +
facet_grid(.~.sign)
ridgeplot(gseMF, showCategory=200) +
labs(x = "Deviation from Baseline AWUI")## Picking joint bandwidth of 0.00143
emapplot(enrichplot::pairwise_termsim(gseMF), showCategory=Inf)
df_genesets <- gseMF@result
for (i in seq(nrow(df_genesets))) {
gseaplot(gseMF, by='all', geneSetID=i, title=df_genesets[i, "Description"]) %>%
print()
}
Conclusion
WUI appears to be sufficient to capture the major transcript-level action we had seen previously with contrasting simply proximal and distal transcripts.
Runtime Details
Session Info
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] org.Hs.eg.db_3.14.0 AnnotationDbi_1.56.1
## [3] clusterProfiler_4.2.0 sparseMatrixStats_1.6.0
## [5] Matrix_1.3-4 SummarizedExperiment_1.24.0
## [7] Biobase_2.54.0 GenomicRanges_1.46.0
## [9] GenomeInfoDb_1.30.0 IRanges_2.28.0
## [11] S4Vectors_0.32.0 BiocGenerics_0.40.0
## [13] MatrixGenerics_1.6.0 matrixStats_0.61.0
## [15] ggbeeswarm_0.6.0 plotly_4.10.0
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## [19] stringr_1.4.0 dplyr_1.0.8
## [21] purrr_0.3.4 readr_2.1.1
## [23] tidyr_1.1.4 tibble_3.1.7
## [25] ggplot2_3.3.5 tidyverse_1.3.1
## [27] magrittr_2.0.3
##
## loaded via a namespace (and not attached):
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## Conda Environment YAML
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prefix: /Users/mfansler/miniconda3